Extraction and classification of three cortical neuron types (Mensi et al. 2012)

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Accession:143148
This script proposes a new convex fitting procedure that allows the parameters estimation of a large class of stochastic Integrate-and-Fire model upgraded with spike-triggered current and moving threshold from patch-clamp experiments (i.e. given the injected current and the recorded membrane potential). This script applies the method described in the paper to estimate the parameters of a reference model from a single voltage trace and the corresponding input current and evaluate the performance of the fitted model on a separated test set.
Reference:
1 . Mensi S, Naud R, Pozzorini C, Avermann M, Petersen CC, Gerstner W (2012) Parameter extraction and classification of three cortical neuron types reveals two distinct adaptation mechanisms. J Neurophysiol 107:1756-75 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Hippocampus CA1 pyramidal GLU cell; Hippocampus CA3 pyramidal GLU cell; Neocortex fast spiking (FS) interneuron; Neocortex spiking regular (RS) neuron;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: MATLAB;
Model Concept(s):
Implementer(s): Mensi, Skander [skander.mensi at epfl.ch];
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell; Hippocampus CA3 pyramidal GLU cell;
function [M] = build_M_matrix(V,spike,t_refr,nbr_bink,bink_size,sampling_freq)

timestep = 1e3/sampling_freq;
M = zeros(nbr_bink,length(V)+200000);
spike = spike + round((t_refr)/timestep);

for i=1:length(spike)
    for j=1:nbr_bink
        if(j==1)
            M(j,spike(i):spike(i)+bink_size(j)-1) = ...
                M(j,spike(i):spike(i)+bink_size(j)-1) + ones(1,bink_size(j));
        else
            M(j,spike(i)+sum(bink_size(1:(j-1))):spike(i)+sum(bink_size(1:(j-1)))+bink_size(j)-1) = ...
                M(j,spike(i)+sum(bink_size(1:(j-1))):spike(i)+sum(bink_size(1:(j-1)))+bink_size(j)-1) + ones(1,bink_size(j));
        end
    end
end

M = M(:,1:length(V));

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